Prof. Xiaohua Hu Profile

Prof. Xiaohua Hu

at Drexel Univ

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Proceedings Article | 21 March 2003 Paper

Using attribute value lattice to find closed frequent itemsets

Tsau Lin, Xiaohua Hu, Eric Louie

Proceedings Volume 5098, (2003) https://doi.org/10.1117/12.486732

KEYWORDS: Raster graphics, Databases, Mining, Algorithm development, Data mining, Detection and tracking algorithms, Biological research, Binary data, Computer science, Information science

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Finding all closed frequent itemsets is a key step of association rule mining since the non-redundant association rule can be inferred from all the closed frequent itemsets. In this paper we present a new method for finding closed frequent itemsets based on attribute value lattice. In the new method, we argue that vertical data representation and attribute value lattice can find all closed frequent itemsets efficiently, thus greatly improve the efficiency of association rule mining algorithm. We discuss how these techniques and methods are applied to find closed frequent itemsets. In our method, the data are represented vertically; each frequent attribute value is associated with its granule, which is represented as a hybrid bitmap. Based on the partial order defined between the attribute values among the databases, an attribute value lattice is constructed, which is much smaller compared with the original databases. Instead of searching all the items in the databases, which is adopted by almost all the association rule algorithms to find frequent itemsets, our method only searches the attribute-value lattice. A bottom-up breadth-first approach is employed to search the attribute value lattice to find the closed frequent itemsets.

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